Blood volume sensitive laminar fMRI with VASO in human hippocampus: Capabilities and biophysical challenges at clinical 7T scanners
Authors/Creators
Description
Abstract
Sub-millimeter resolution functional magnetic resonance imaging (fMRI) at ultra-high field (≥ 7T) has offered an unprecedented opportunity to probe mesoscopic computations at a columnar or laminar level. However, its application has been primarily restricted to the neocortex. Inferior brain regions, particularly the hippocampus (HC), are challenging targets for laminar fMRI. Recent developments in acquisition methods have shown the feasibility of laminar recordings in the HC using gradient-echo blood oxygenation level-dependent (BOLD) contrast. Nonetheless, the spatial specificity of the BOLD signal is compromised by the draining veins’ bias. Cerebral blood volume (CBV)-sensitive sequences including vascular space occupancy (VASO) have emerged as a promising approach to capture the laminar activity with mitigated venous bias. Yet, its feasibility in the HC is unclear and challenged by methodological constraints. Here, we optimized VASO to mitigate the macrovasculature contribution in HC. By evaluating a series of advanced acquisition strategies tailored to HC, we obtained improved VASO signal quality with minimal artifacts. The optimized protocol was further validated with an autobiographical memory task. Our findings show that combining the high detection power of gradient-echo BOLD with the vein-bias-mitigated VASO contrast allows for differentiation between neural activity-related BOLD signals and those biased by draining veins. These results demonstrate the feasibility of submillimeter VASO acquired with conventional 7T scanners in the HC to map the circuit-level mechanisms of memory retrieval across HC subfields, laying a foundation to investigate the microcircuitry of HC-driven complex cognitive functions and their alterations in neurodegeneration and epilepsy.
Files
reviewer_response_analysis_outputs.zip
Files
(25.0 GB)
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Additional details
Funding
- European Research Council
- 864164
- Mercator Research Center Ruhr
- Ko-2021-0010
- National Institute of Neurological Disorders and Stroke
- #ZIAMH002783
- National Institute of Mental Health
- #ZICMH002884
Software
- Repository URL
- https://gitlab.ruhr-uni-bochum.de/neuropsy/vaso_hc
- Programming language
- MATLAB , R , Shell